Building a business case for data quality can be tricky. We need to start with an understanding of why data quality matters in our context.

One of the biggest challenges – as data quality professionals we make assumptions about other people that hinder our ability to put together a compelling business case.

Faulty assumptions about data quality

How many of these assumptions do you make?

• Everyone understands how important data quality is
• Everyone wants to do something to resolve data quality issues
• Everyone knows what they should be doing to solve data quality problems.
• We don’t need quality data for big data analytics

When we make these assumptions it is easy to put together a weak business case, assuming that key business decision-makers will understand the value.

A strong business case for data quality will not suffer these delusions.

In practice, the link between poor-quality data and poor business performance is typically not well understood.

Rather, it should spell out the link between poor-quality data and business issues.

Choose specific, relevant examples

Having trouble with outstanding invoices?

What percentage of invoices are not paid due to errors in key data? What is the average size of unpaid invoices? For how many days do you carry the extra debt? How much does that cost you? What is the administrative cost of correcting and resubmitting each invoice?

By speaking to specific examples the business case becomes specific.

Data Quality Assessment

A data quality assessment can be an affordable tool to quantify the impact of poor-quality data on your business.

What percentage of invoices do not include a valid VAT number, or a purchase order, or any other attributes.

Data quality should be defined as data that supports the required business goals.

Let’s delve deeper: how does data quality add value? It’s not just about numbers; it’s about the impact on your bottom line.

Tools help to expose data anomalies so that the impact can be quantified with business stakeholders. This approach helps business stakeholders to visualize how poor quality data is impacting key processes, and also to dismiss anomalies that have no (or minimal) impact so that these don’t skew the business case. Concrete metrics make for a strong business case.

What data quality delusions have you seen?

Think about it: Why is clean data important? The analogy between water and poison paints a vivid picture of its significance in your operations.